| Literature DB >> 32271281 |
Martine Remy-Jardin1,2, Jean-Baptiste Faivre1, Rainer Kaergel3, Antoine Hutt1, Paul Felloni1, Suonita Khung1, Anne-Laure Lejeune1, Jessica Giordano1, Jacques Remy1.
Abstract
The radiologic community is rapidly integrating a revolution that has not fully entered daily practice. It necessitates a close collaboration between computer scientists and radiologists to move from concepts to practical applications. This article reviews the current littérature on machine learning and deep neural network applications in the field of pulmonary embolism, chronic thromboembolic pulmonary hypertension, aorta, and chronic obstructive pulmonary disease.Entities:
Mesh:
Year: 2020 PMID: 32271281 DOI: 10.1097/RTI.0000000000000492
Source DB: PubMed Journal: J Thorac Imaging ISSN: 0883-5993 Impact factor: 3.000